Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy

Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches

Key Features
Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches
Develop and deploy privacy-preserving ML pipelines using open-source frameworks
Gain insights into confidential computing and its role in countering memory-based data attacks

Book Description
Privacy regulations are evolving each year and compliance with privacy regulations is mandatory for every enterprise. Machine learning engineers are required to not only analyze large amounts of data to gain crucial insights, but also comply with privacy regulations to protect sensitive data. This may seem quite challenging considering the large volume of data involved and lack of in-depth expertise in privacy-preserving machine learning.
This book delves into data privacy, machine learning privacy threats, and real-world cases of privacy-preserving machine learning, as well as open-source frameworks for implementation. You'll be guided through developing anti-money laundering solutions via federated learning and differential privacy. Dedicated sections also address data in-memory attacks and strategies for safeguarding data and ML models. The book concludes by discussing the necessity of confidential computation, privacy-preserving machine learning benchmarks, and cutting-edge research.
By the end of this machine learning book, you'll be well-versed in privacy-preserving machine learning and know how to effectively protect data from threats and attacks in the real world.

What you will learn
Study data privacy, threats, and attacks across different machine learning phases
Explore Uber and Apple cases for applying differential privacy and enhancing data security
Discover IID and non-IID data sets as well as data categories
Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks
Understand secure multiparty computation with PSI for large data
Get up to speed with confidential computation and find out how it helps data in memory attacks

Who this book is for
This book is for data scientists, machine learning engineers, and privacy engineers who have working knowledge of mathematics as well as basic knowledge in any one of the ML frameworks (TensorFlow, PyTorch, or scikit-learn).